System and Method for Using Stores as Receiving Points for Third Party, E-Commerce Suppliers

ABSTRACT

Systems, methods and computer-readable media for providing e-commerce suppliers an alternative shipping and distribution system based on real-time sales and demand being coupled with iterative machine learning processes. As an example, an e-commerce supplier can contract with a retailer to have products sold in retail locations belonging to the retailer. However, rather than the retailer providing to the e-commerce supplier a quoted price for acquiring the merchandise, the retailer can provide two prices: one for if the e-commerce supplier delivers the merchandise to a distribution center, and another if the e-commerce supplier delivers the merchandise directly to a retail location, from which the retailer will redistribute the merchandise to other retail locations.

BACKGROUND 1. Technical Field

The present disclosure relates to receiving merchandise from thirdparty, e-commerce suppliers at a retail location, then distributing thereceived merchandise to other retail locations.

2. Introduction

Product distribution systems often follow a model where the manufacturerof a product delivers a finished product to a distribution center for aretailer, then the retailer transports the product from the distributioncenter to nearby retail locations. For example, a manufacturer oftoothpaste who has contracted with a retailer to sell the toothpaste inretail stores will deliver a truckload of toothpaste product to adistribution center associated with the retailer. The retailer will thensend trucks from the distribution center to retail locations for sale tocustomers, each truck having some toothpaste as well as other products.In some cases, suppliers will have their own retail locations, termed“outlet stores.” However, for e-commerce suppliers, sale of productswithin a brick and mortar retail location is generally not possibleunless the e-commerce suppliers contract with retailers and deliver themerchandise to distribution centers.

SUMMARY

An exemplary method for practicing concepts disclosed herein can providethird party suppliers multiple price costs for distributing a productfrom distinct points of distribution. Such a method can include:receiving, at a server, historical sales data associated with a thirdparty e-commerce product; applying a machine learning algorithm to thehistorical sales data, to yield a predicted demand quantity for thethird party e-commerce product at a plurality of retail locations,wherein the machine learning algorithm is updated on a periodic basis;receiving an offer for the predicted demand quantity of the third partye-commerce product from a third party supplier; calculating, using aprocessor of the server, a first shipping cost for receiving thepredicted demand quantity from the third party supplier at a singleretail location in the plurality of retail locations, and subsequentlyredistributing the predicted demand quantity to remaining retaillocations in the plurality of retail locations; calculating, using theprocessor, a second shipping cost for receiving the predicted demandquantity from the third party supplier at a distribution center andredistributing the predicted demand quantity to remaining retaillocations in the plurality of retails locations; and transmitting, inresponse to the offer, the first shipping cost and the second shippingcost to the third party supplier.

An exemplary system configured to practice principles described hereincan be configured to include: a processor; and a computer-readablestorage medium having instructions stored which, when executed by theprocessor, cause the processor to perform operations comprising:applying a machine learning algorithm to historical sales data for athird party e-commerce product, to yield a predicted demand quantity forthe third party e-commerce product at a plurality of retail locations;calculating a first shipping cost for receiving the predicted demandquantity from a third party supplier of the third party e-commerceproduct at a single retail location in the plurality of retaillocations, and subsequently redistributing the predicted demand quantityto remaining retail locations in the plurality of retail locations;calculating a second shipping cost for receiving the predicted demandquantity from the third party supplier at a distribution center andredistributing the predicted demand quantity to remaining retaillocations in the plurality of retails locations; and transmitting thefirst shipping cost and the second shipping cost to the third partysupplier.

An exemplary non-transitory computer-readable storage medium configuredaccording to principles described herein can having instructions storedwhich, when executed by a computing device, cause the computing deviceto perform operations including: applying a machine learning algorithmto historical sales data for a third party e-commerce product, to yielda predicted demand quantity for the third party e-commerce product at aplurality of retail locations; calculating a first shipping cost forreceiving the predicted demand quantity from a third party supplier ofthe third party e-commerce product at a single retail location in theplurality of retail locations, and subsequently redistributing thepredicted demand quantity to remaining retail locations in the pluralityof retail locations; calculating a second shipping cost for receivingthe predicted demand quantity from the third party supplier at adistribution center and redistributing the predicted demand quantity toremaining retail locations in the plurality of retails locations; andtransmitting the first shipping cost and the second shipping cost to thethird party supplier.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a first exemplary product distribution system;

FIG. 2 illustrates a second exemplary product distribution system;

FIG. 3 illustrates a flowchart illustrating using machine learning inparallel with time series and regression modeling to predict demand;

FIG. 4 illustrates an exemplary method embodiment; and

FIG. 5 illustrates an exemplary computer system.

DETAILED DESCRIPTION

As used herein, the term “retailer” or “retail marketplace” may refer toan entity that offers products for sale to consumers, sometimes referredto as end users, on a retail level. The products offered for sale byretailers may include its own products, products purchased frompartners, or products offered by partners for sale even though title tothe products has not been formally transferred to the retailer. A store,or retail location, as used herein is a “brick-and-mortar” store,meaning that consumers may visit the location to select merchandise forpurchase.

As used herein, the term “distribution center” may refer to an operationthat includes a warehouse for storing and managing inventory ofproducts. In some configurations, the distribution centers mayphysically manage inventory of product offered for sale by others,including partners, third party suppliers (including e-commercesuppliers), and retailers. That is, distribution centers may fulfillshipping orders from retailers and partners.

As used herein, transports and transport vehicles are mechanisms usedfor moving goods between distribution centers and retail locations.While in some cases this can be a direct distribution (i.e., a truckmoves goods directly from the distribution center to the retaillocation), in other cases the distribution can be indirect (i.e., atrain moves goods from a distribution center to an intermediatedistribution center, where a truck receives the goods and delivers themto retail locations). Exemplary transports can therefore include trucksand trains, as well as air transport such as planes and aerial drones.Automated, self-driving trucks and/or drones are likewise within thescope of this disclosure.

Systems, methods and computer-readable media configured as disclosedherein can provide e-commerce suppliers an alternative shipping anddistribution system based on real-time sales and demand being coupledwith iterative machine learning processes. As an example, an e-commercesupplier can contract with a retailer to have products sold in retaillocations belonging to the retailer. However, rather than the retailerproviding to the e-commerce supplier a quoted price for acquiring themerchandise, the retailer can provide two prices: one for if thee-commerce supplier delivers the merchandise to a distribution center,and another if the e-commerce supplier delivers the merchandise directlyto a retail location, from which the retailer will redistribute themerchandise to other retail locations. The price quoted by the retailerfor the e-commerce supplier delivering merchandise directly to theretail location can use real-time factors such as sales, an affinityscore to other similar products, demographics of a sales area, calendarinformation, demand for the product calculated using time seriesmodeling, regression modeling, and/or machine learning modeling, and anavailability of the products.

Consider an additional example. An e-commerce product supplier, who doesnot have a brick and mortar retail location, wishes to sell products ina brick and mortar store. The e-commerce product supplier contracts witha retailer that has various retail locations. Under some productdistribution systems, the retailer and the e-commerce product supplierwould negotiate a price for the product which would require thee-commerce product supplier to deliver the product to a distributioncenter associated with the retailer. The retailer would then move theproduct from the distribution center to desired retail locations basedon predicted demand for the product at those retail locations. However,such systems often require the e-commerce product supplier to spendpotential profits on transporting the merchandise to the distributioncenter when a retail location is located closer to the e-commercesupplier. In addition, by using two distribution systems (one for athird party shipping company to deliver the merchandise to thedistribution center, and another for the retailer to distribute themerchandise from the distribution center to the retailers),inefficiencies can occur.

To remedy waste associated with such distribution mechanisms, systemsconfigured as disclosed herein allow the retailer to provide multiplequotes to the e-commerce product supplier based on where merchandise isdelivered. For example, the retailer may provide to the e-commerceproduct supplier a quote for purchasing a product if the e-commerceproduct supplier delivers the product to the distribution center, and adistinct quote if the e-commerce product supplier delivers the productto a retail location. In the latter case, the retailer would then assigna transport vehicle to collect merchandise which needs to be distributedto other retail locations from the retail location at which the productwas delivered. Once the e-commerce product supplier receives the offers,they may evaluate the prices and locations of the retail locations. Itmay be determined that delivering products to a retail location ratherthan the distribution center makes business sense. If, for example, thee-commerce product supplier has a factory near to a retail location ofthe retailer, the e-commerce product supplier may determine thatdelivering products to the retail location makes more sense thandelivering the product to the distribution center.

When the product is delivered to a retail location, the retailer is thenresponsible for collecting the delivered merchandise and distributingthe products to other retail locations. Generally, this takes the formof a transport vehicle being assigned to collect the merchandise fromthe retail location where the product was delivered (minus any productwhich is to remain in that retail location), move the product to adistribution center, and finally redistribute the product to the otherretail locations. In another embodiment, the product may be distributedfrom one retail locations to another. In this manner, the e-commerceproduct supplier may maximize reliance on the distribution system of theretailer throughout the entire product distribution process, rather thanrelying on a third-party shipping company (such as UPS®, FederalExpress®, etc.).

Identifying the amount of merchandise which is to remain at the initialretail location where merchandise is delivered by the e-commerce productsupplier, or the amount of merchandise which needs to be collected andredistributed to other retail locations, can be based on using multipleprediction models in parallel. These models can, for example, rely onreal-time data regarding current store inventories, historical salesinformation, calendar information, marketing information, affinityscores, and machine-learning to predict what the demand will be for aproduct at a given location. Exemplary models which can be performed inparallel can include time-series models, regression models, and/ormachine learning algorithms such as a gradient boost model. The quoteswhich are then provided by the retailer to the e-commerce productprovider for the merchandise can use the predicted demand for eachstore, as well as real-time data associated with the costs oftransporting merchandise from one location to another.

The affinity score used to calculate predicted demand and/or costs canbe based on how similar an item is to other products. To determine howsimilar a product is to other products, a similarity index can be used.In the similarity index, attributes such as color, weight, volume,product type, color, brand, etc. are registered. In making a similaritycomparison between products, each of the attributes in the similarityindex can be weighted and used to calculate the level of similarity ofthe products. If the products have a high enough level of calculatedsimilarity, they may be considered replacement products. For example,comparing a wooden chair to a candy bar using the similarity index couldresult in an affinity score which is computed: affinity score=0.2*colordifference+0.2*brand difference+0.2*size difference+0.4*product typedifference. Because a chair and a candy bar are likely to have largedistinctions in brand, size, and product types, the affinity score inthis example will be quite large (indicating that the products are notsimilar). By contrast, a similar comparison of two types of candy barsis likely to result in smaller distinctions, and therefore a smalleraffinity score will result (indicating that the products are moresimilar) should the same weighted equation (or a similar equation) beused to determine affinity of a product to other products.

Demographics can likewise be used by the time series models, theregression models, and the machine learning models to determinepredicted demand. For example, if a product is associated with a ruralpopulation, then the predicted demand for the product will be higher inrural retail locations than in urban retail locations Likewise, if theproduct is associated with a particular ethnic group, then the predicteddemand for the product may be higher in retail locations near populationcenters having a significant population of that particular ethnic group.Exemplary demographics which can be used by the system to calculatepredicted demand include age of common purchasers of the product,ethnicities associated with the product, wealth of common purchasers ofthe product, education of common purchasers of the product, andgeographic locations of common purchasers of the product.

A time series model uses a series of data points indexed in time order,where between each data point is an equal amount of time. By enteringprevious data points into the model, future values can be predicted. Aregression model can be used in combination with a time series model totest relationships between variables and data points. These modelingtechniques can be used to predict, based on historical and real-timedata, demand for a product. Machine learning models make similarpredictions based on the historical data, but overcome followingstrictly static program instructions by making data driven predictionsor decisions. As predictions are made and subsequent results received,the machine learning model iteratively updates its parameters, improvingthe code used to make determinations.

By performing time series and regression modeling in parallel withmachine learning models, systems configured according to this disclosurecan select from the predictive model which has consistently provided thebest results, or which has provided the most consistent resultsrecently. For example, over long periods of time, the machine learningmodels might provide higher accuracy than the time series and regressionmodeling. However, when something changes which dramatically effects therate of sales for an item (i.e., moving the item to a different shelf orlocation in the store, or a negative news report effects consumerdemand), the time series and regression modeling might be betterequipped to accurately model the predicted demand.

In some configurations, the iterative updates to the machine learningalgorithm are tailored based on distinct aspects of the data beingreceived. For example, in some configurations, the timeframe for whichdata is available, as well as the seasonality of the data (i.e., howoften certain patterns appear in the data, such as weekly, monthly,quarterly, annually), are used to define sets of data and train themachine learning algorithm. In a preferred configuration, the sets usedto train the algorithm represent both a good portion of the overall dataas well as the seasonality of the data. For example, if the system hasthree years of data with an annual seasonality/pattern, the system canuse two years as a training set and one year as a testing set, whereasif the three years of data had a monthly seasonality/pattern, the systemcould use 32 months as training data and four months as a testing set.The seasonality in the data can also contribute to the frequency of theiterative updates. Fast changing items and categories would require morefrequent updates compared to more stable items and categories. Eachiteration would bring in, for example, newly added historical data, andfrom that newly added historical data, the machine learning algorithmcan provide updated forecasts of demand.

By using iterative machine learning, the supply chain can become moreefficient and robust, and the supply chain can adapt to changingdemands, supply, etc. For example, the disclosed systems and methodsimprove ease of access because there are more retail locations thandistribution centers. In particular, this can be a benefit to small,local businesses as they can easily drop off their merchandise at thestores using their personal vehicles, rather than relying on carriershipping. Moreover, those small businesses can have more flexibility inscheduling deliveries, as retail locations can have more flexibledelivery hours than that of a distribution center. In addition, in caseswhere the store is going to retain the delivered product, the store willhave already received the product directly from the supplier, which canbe helpful when products may have a short shelf life (i.e., locallygrown food/produce).

The concepts disclosed herein can also be used to improve the computingsystems which are performing, or enabling the performance, of thedisclosed concepts. For example, information associated with routes,deliveries, truck cargo, distribution center inventory or requirements,retail location inventory or requirements, etc., can be generated bylocal computing devices. In a standard computing system, the informationwill then be forwarded to a central computing system from the localcomputing devices. However, systems configured according to thisdisclosure can improve upon this “centralized” approach.

One way in which systems configured as disclosed herein can improve uponthe centralized approach is combining the data from the respective localcomputing devices prior to communicating the information from the localcomputing devices to the central computing system. For example, a trucktraveling from a distribution center to a retail location may berequired to generate information about (1) the route being travelled,(2) space available in the truck for additional goods, (3) conditionswithin the truck, etc. Rather than transmitting each individual piece ofdata each time new data is generated, the truck processor can cache thegenerated data for a period of time and combine the generated data withany additional data which is generated within the period of time. Thiswithholding and combining of data can conserve bandwidth due to thereduced number of transmissions, can save power due to the reducednumber of transmissions, and can increase accuracy due toholding/verifying the data for a period of time prior to transmission.

Another way in which systems configured as disclosed herein can improveupon the centralized approach is adapting a decentralized approach,where data is shared among all the individual nodes/computing devices ofthe network, and the individual computing devices perform calculationsand determinations as required. In such a configuration, the same truckdescribed above can be in communication with the retail location and thedistribution center, and can make changes to the route, destination,pickups/deliveries, etc., based on data received and processed whileenroute between locations. Such a configuration may be more power and/orbandwidth intensive than a centralized approach, but can result in amore dynamic system because of the ability to modify assignments andrequirements immediately upon making that determination. In addition,such a system can be more secure, because there are multiple points offailure (rather than a single point of failure in a centralized system).

It is worth noting that a “hybrid” system might be more suitable forsome specific configurations. In this approach, a part of thenetwork/system would be using the centralized approach (which can takeadvantage of the bandwidth savings described above), while the rest ofthe system is utilizing a de-centralized approach (which can takeadvantage of the flexibility/increased security described above). Forinstance, the trucks could be connected to a central server at thedistribution center, while that server is connected to a decentralizednetwork of store computers.

Various embodiments of the disclosure are described in detail below.While specific implementations are described, it should be understoodthat this is done for illustration purposes only. Other components andconfigurations may be used without parting from the spirit and scope ofthe disclosure. In addition, individual components and concepts from thespecific configurations and embodiments disclosed herein may be used byother configurations and embodiments disclosed herein, or removed fromthe configurations in which they are described, without parting from thespirit of this disclosure.

FIG. 1 illustrates a first exemplary product distribution system 100. Inthis example 100, a third party e-commerce supplier 104 receives andprocesses orders from the Internet 102. The third party e-commercesupplier 104 also negotiates to have their product(s) sold in retailstores 108-112 belonging to a retailer. To get the product(s) to theretail stores 108-112, the third party e-commerce supplier 104 arrangesfor the product(s) to be delivered to a distribution center 106associated with the retailer. The retailer then transports theproduct(s) from the distribution center 106 to the individual retailstores 108-112.

FIG. 2 illustrates an alternative exemplary product distribution system200. In this example 200, the third party e-commerce supplier 104continues to receive and process orders from the Internet 102, and alsohas negotiated with a retailer to have their product(s) sold in retailstores 108-112. However, unlike the distribution system 100 of FIG. 1,in this example 200 the third party e-commerce supplier 104 delivers theproduct(s) to a retail location, Store A 112. Once the product(s) arereceived at Store A 112, the retailer collects any merchandise notneeded at Store A 112 and transports that remaining merchandise to adistribution center 106 associated with the retailer. The retailer thentransports product(s) from the distribution center 106 to the remainingretail locations 108, 110 according to the predicted demand for theproduct(s) at the respective stores. In this manner, the third partye-commerce supplier 104 can (potentially) save time and money in gettingthe product(s) to the retailer, and the retailer does not need todistribute those product(s) to Store A 112. In addition, the product maybe distributed from Store A to Stores B and C without going to thedistribution center.

FIG. 3 illustrates a flowchart 300 illustrating using machine learning312 in parallel with time series and regression modeling 310 to predictdemand for a product. In this example 300, both the time series andregression modeling 310 and the machine learning 312 have access toinformation such as historical order data 302, affinity scores 304 whichreflect how similar the product in question is to other products,calendar information 306 (indicating weekdays versus weekends, holidays,seasons, etc.), and demographic information 308 associated with theproduct. Additional information used by the time series and regressionmodels 310, as well as the machine learning models 312, can includereal-time inventory levels of the product in the stores, real-timeinventory levels in distribution centers, real-time inventory intransit, time since release of the product (i.e., demand for the productmay decrease from release), marketing information, and/or informationassociated with a replacement product being released soon (i.e., end ofseason products, new movie releases, etc.).

As the machine learning models 312 make demand predictions in parallelwith the time series and regression modeling 310, the Rsystem makes aselection of a predictive model 314. This selection can, for example, bebased on overall patterns of prediction or can be based on patterns ofprediction associated with specific factors. For example, a particularmachine learning model may excel at predictions over holidays, but maybe of lesser quality than other models for other days of the yearLikewise, a particular model may have the best overall predictionrecord, but may have been inaccurate over the previous two weeks (orother period of time). In such a scenario, the system may select adistinct model to calculate predicted demand for the respective retailstore locations.

The system then calculates the on-hand future inventory 316 based on thepredicted demand and the product availability 318. In other words, thesystem is determining how many product units should be at each retaillocation based on (1) the amount available 318 from the e-commerceproduct supplier and (2) the predicted demand calculated by the chosenmodel 314.

Using that predicted on-hand future inventory 316, the system calculatesthe distribution costs 320, which can in turn be used by the retailer inproviding quotes to the e-commerce product supplier. The on-hand futureinventory 316 prediction and demand are saved and, over time, comparedto actual sales 322. Using that information, the machine learning models312 are iteratively updated 324, and improve the way the models use thedata 302-308 to predict demand. These determinations may be based ongroupings of the retail locations and distribution centers. The retailstores and distribution centers may be organized into groups or regionsbased on geography or other considerations. A distribution center mayservice the retail locations in its region. The inventory and costs maybe determined based on the costs to transport the products within oracross regions.

FIG. 4 illustrates an exemplary method embodiment. The steps outlinedherein are exemplary and can be implemented in any combination thereof,including combinations that exclude, add, or modify certain steps. Themethod can, for example, be performed by a server or other computingsystem configured to receive multiple real-time data feeds and/orconfigured to retrieve data simultaneously from multiple databases ordata sources (such as inventory information from retail locations), andcan provide third party suppliers multiple price costs for distributinga product from distinct points of distribution. The exemplary method caninclude receiving, at a server, historical sales data associated with athird party e-commerce product (402). The server can apply applying amachine learning algorithm to the historical sales data, to yield apredicted demand quantity for the third party e-commerce product at aplurality of retail locations, wherein the machine learning algorithm isupdated on a periodic basis (404), and receive an offer for thepredicted demand quantity of the third party e-commerce product from athird party supplier (406).

The server can then calculate, using a processor, a first shipping costfor receiving the predicted demand quantity from the third partysupplier at a single retail location in the plurality of retaillocations, and subsequently redistributing the predicted demand quantityto remaining retail locations in the plurality of retail locations(408). The server can also calculate, using the processor, a secondshipping cost for receiving the predicted demand quantity from the thirdparty supplier at a distribution center and redistributing the predicteddemand quantity to remaining retail locations in the plurality ofretails locations (410) and transmit, in response to the offer, thefirst shipping cost and the second shipping cost to the third partysupplier (412). As noted above, the costs may be based on historicaldata for the same or similar items using the affinity score.

In certain configurations, the method can be augmented to furtherinclude receiving, at the server, electronic messages indicatinginventory amounts of the third party e-commerce product from theplurality of retail locations, wherein the machine learning algorithmuses the inventory amounts in predicting the predicted demand quantityfor the third party e-commerce product at the plurality of retaillocations.

In yet other configurations, the method can be modified to furtherinclude applying, in parallel to the machine learning algorithm, thehistorical sales data to a time series and regression model, to yield asecond predicted demand quantity in addition to the predicted demandquantity generated by the machine learning algorithm; and selecting,based on historical accuracy, one of the predicted demand quantitygenerated by the machine learning algorithm and the second predicteddemand quantity, for use in calculating the first shipping cost and thesecond shipping cost.

In some configurations, the machine learning algorithm can include:combining time series information contained within the historical salesdata with a plurality of regression models, to yield a plurality ofsales forecasts; recording the plurality of sales forecasts in adatabase, to yield stored sales forecasts; selecting, based on pastperformance of the machine learning algorithm, a sales forecast from theplurality of sales forecasts; and upon receiving additional data,updating the machine learning algorithm using the additional data, thehistorical sales data, and the stored sales forecasts.

The difference between the first shipping cost and the second shippingcost can represent a cost of transporting goods from the single retaillocation to the distribution center and subsequently from thedistribution center to the remaining retail locations. In addition, itis noted that the distribution center does not sell goods directly tocustomers, while the plurality of retail locations do sell goodsdirectly to customers.

With reference to FIG. 5, an exemplary system 500 which can be used topractice the concepts disclosed herein. The exemplary system 500illustrated contains a processing unit (CPU or processor) 520 and asystem bus 510 that couples various system components including thesystem memory 530 such as read only memory (ROM) 540 and random accessmemory (RAM) 550 to the processor 520. The system 500 can include acache of high speed memory connected directly with, in close proximityto, or integrated as part of the processor 520. The system 500 copiesdata from the memory 530 and/or the storage device 560 to the cache forquick access by the processor 520. In this way, the cache provides aperformance boost that avoids processor 520 delays while waiting fordata. These and other modules can control or be configured to controlthe processor 520 to perform various actions. Other system memory 530may be available for use as well. The memory 530 can include multipledifferent types of memory with different performance characteristics. Itcan be appreciated that the disclosure may operate on a computing device500 with more than one processor 520 or on a group or cluster ofcomputing devices networked together to provide greater processingcapability. The processor 520 can include any general purpose processorand a hardware module or software module, such as module 1 562, module 2564, and module 3 566 stored in storage device 560, configured tocontrol the processor 520 as well as a special-purpose processor wheresoftware instructions are incorporated into the actual processor design.The processor 520 may essentially be a completely self-containedcomputing system, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric.

The system bus 510 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 540 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 500, such as during start-up. The computing device 500further includes storage devices 560 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 560 can include software modules 562, 564, 566 forcontrolling the processor 520. Other hardware or software modules arecontemplated. The storage device 560 is connected to the system bus 510by a drive interface. The drives and the associated computer-readablestorage media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputing device 500. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangiblecomputer-readable storage medium in connection with the necessaryhardware components, such as the processor 520, bus 510, display 570,and so forth, to carry out the function. In another aspect, the systemcan use a processor and computer-readable storage medium to storeinstructions which, when executed by the processor, cause the processorto perform a method or other specific actions. The basic components andappropriate variations are contemplated depending on the type of device,such as whether the device 500 is a small, handheld computing device, adesktop computer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk560, other types of computer-readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 550, and read only memory (ROM) 540, may also be used in theexemplary operating environment. Tangible computer-readable storagemedia, computer-readable storage devices, or computer-readable memorydevices, expressly exclude media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 500, an inputdevice 590 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 570 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 500. The communications interface 580generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Various modifications and changes may be made to theprinciples described herein without following the example embodimentsand applications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure.

We claim:
 1. A method for providing third party suppliers multiple pricecosts for distributing a product from distinct points of distribution,the method comprising: receiving, at a server, historical sales dataassociated with a third party e-commerce product; applying a machinelearning algorithm to the historical sales data, to yield a predicteddemand quantity for the third party e-commerce product at a plurality ofretail locations, wherein the machine learning algorithm is updated on aperiodic basis; receiving an offer for the predicted demand quantity ofthe third party e-commerce product from a third party supplier;calculating, using a processor of the server, a first shipping cost forreceiving the predicted demand quantity from the third party supplier ata single retail location in the plurality of retail locations;subsequently redistributing the predicted demand quantity from thesingle retail location to remaining retail locations in the plurality ofretail locations; calculating, using the processor, a second shippingcost for receiving the predicted demand quantity from the third partysupplier at a distribution center and redistributing the predicteddemand quantity to remaining retail locations in the plurality ofretails locations; and transmitting, in response to the offer, the firstshipping cost and the second shipping cost to the third party supplier.2. The method of claim 1, further comprising: receiving, at the server,electronic messages indicating inventory amounts of the third partye-commerce product from the plurality of retail locations, wherein themachine learning algorithm uses the inventory amounts in predicting thepredicted demand quantity for the third party e-commerce product at theplurality of retail locations.
 3. The method of claim 1, furthercomprising: applying, in parallel to the machine learning algorithm, thehistorical sales data to a time series and regression model, to yield asecond predicted demand quantity in addition to the predicted demandquantity generated by the machine learning algorithm; and selecting,based on historical accuracy, one of the predicted demand quantitygenerated by the machine learning algorithm and the second predicteddemand quantity, for use in calculating the first shipping cost and thesecond shipping cost.
 4. The method of claim 1, wherein the machinelearning algorithm further comprises: combining time series informationcontained within the historical sales data with a plurality ofregression models, to yield a plurality of sales forecasts; recordingthe plurality of sales forecasts in a database, to yield stored salesforecasts; selecting, based on past performance of the machine learningalgorithm, a sales forecast from the plurality of sales forecasts; andupon receiving additional data, updating the machine learning algorithmusing the additional data, the historical sales data, and the storedsales forecasts.
 5. The method of claim 1, wherein a difference betweenthe first shipping cost and the second shipping cost represents a costof transporting goods from the single retail location to thedistribution center and subsequently from the distribution center to theremaining retail locations.
 6. The method of claim 1, wherein thedistribution center does not sell goods directly to customers andwherein the plurality of retail locations do sell goods directly tocustomers.
 7. The method of claim 1, wherein the server is configured toreceive real-time updates of amounts of the third party e-commerceproduct at the plurality of retail locations.
 8. A system comprising: aprocessor; and a computer-readable storage medium having instructionsstored which, when executed by the processor, cause the processor toperform operations comprising: applying a machine learning algorithm tohistorical sales data for a third party e-commerce product, to yield apredicted demand quantity for the third party e-commerce product at aplurality of retail locations; calculating a first shipping cost forreceiving the predicted demand quantity from a third party supplier ofthe third party e-commerce product at a single retail location in theplurality of retail locations, and subsequently redistributing thepredicted demand quantity to remaining retail locations in the pluralityof retail locations; calculating a second shipping cost for receivingthe predicted demand quantity from the third party supplier at adistribution center and redistributing the predicted demand quantity toremaining retail locations in the plurality of retails locations; andtransmitting the first shipping cost and the second shipping cost to thethird party supplier.
 9. The system of claim 8, the computer-readablestorage medium having additional instructions stored which, whenexecuted by the processor, cause the processor to perform operationscomprising: receiving electronic messages indicating inventory amountsof the third party e-commerce product from the plurality of retaillocations, wherein the machine learning algorithm uses the inventoryamounts in predicting the predicted demand quantity for the third partye-commerce product at the plurality of retail locations.
 10. The systemof claim 8, wherein the historical sales data further comprises: anaffinity score for the third party e-commerce product; a calendar ofevents and promotions associated with the third party e-commerceproduct; demographic information associated with each retail location inthe plurality of retail locations.
 11. The system of claim 8, whereinthe machine learning algorithm further comprises: combining time seriesinformation contained within the historical sales data with a pluralityof regression models, to yield a plurality of sales forecasts; recordingthe plurality of sales forecasts in a database, to yield stored salesforecasts; selecting, based on past performance of the machine learningalgorithm, a sales forecast from the plurality of sales forecasts; andupon receiving additional data, updating the machine learning algorithmusing the additional data, the historical sales data, and the storedsales forecasts.
 12. The system of claim 8, wherein a difference betweenthe first shipping cost and the second shipping cost represents a costof transporting goods from the single retail location to thedistribution center and subsequently from the distribution center to theremaining retail locations.
 13. The system of claim 8, wherein thedistribution center does not sell goods directly to customers andwherein the plurality of retail locations do sell goods directly tocustomers.
 14. The system of claim 8, wherein the processor isconfigured to receive real-time updates of amounts of the third partye-commerce product at the plurality of retail locations.
 15. Anon-transitory computer-readable storage medium having instructionsstored which, when executed by a computing device, cause the computingdevice to perform operations comprising: applying a machine learningalgorithm to historical sales data for a third party e-commerce product,to yield a predicted demand quantity for the third party e-commerceproduct at a plurality of retail locations; calculating a first shippingcost for receiving the predicted demand quantity from a third partysupplier of the third party e-commerce product at a single retaillocation in the plurality of retail locations, and subsequentlyredistributing the predicted demand quantity to remaining retaillocations in the plurality of retail locations; calculating a secondshipping cost for receiving the predicted demand quantity from the thirdparty supplier at a distribution center and redistributing the predicteddemand quantity to remaining retail locations in the plurality ofretails locations; and transmitting the first shipping cost and thesecond shipping cost to the third party supplier.
 16. The non-transitorycomputer-readable storage medium of claim 15, having additionalinstructions stored which, when executed by the computing device, causethe computing device to perform operations comprising: receivingelectronic messages indicating inventory amounts of the third partye-commerce product from the plurality of retail locations, wherein themachine learning algorithm uses the inventory amounts in predicting thepredicted demand quantity for the third party e-commerce product at theplurality of retail locations.
 17. The non-transitory computer-readablestorage medium of claim 15, wherein the historical sales data furthercomprises: an affinity score for the third party e-commerce product; acalendar of events and promotions associated with the third partye-commerce product; demographic information associated with each retaillocation in the plurality of retail locations.
 18. The non-transitorycomputer-readable storage medium of claim 15, wherein the machinelearning algorithm further comprises: combining time series informationcontained within the historical sales data with a plurality ofregression models, to yield a plurality of sales forecasts; recordingthe plurality of sales forecasts in a database, to yield stored salesforecasts; selecting, based on past performance of the machine learningalgorithm, a sales forecast from the plurality of sales forecasts; andupon receiving additional data, updating the machine learning algorithmusing the additional data, the historical sales data, and the storedsales forecasts.
 19. The non-transitory computer-readable storage mediumof claim 15, wherein a difference between the first shipping cost andthe second shipping cost represents a cost of transporting goods fromthe single retail location to the distribution center and subsequentlyfrom the distribution center to the remaining retail locations.
 20. Thenon-transitory computer-readable storage medium of claim 15, wherein thedistribution center does not sell goods directly to customers andwherein the plurality of retail locations do sell goods directly tocustomers.